library(tidyverse) # for graphing and data cleaning
library(gardenR) # for Lisa's garden data
library(lubridate) # for date manipulation
library(ggthemes) # for even more plotting themes
library(geofacet) # for special faceting with US map layout
theme_set(theme_minimal()) # My favorite ggplot() theme :)
# Lisa's garden data
data("garden_harvest")
# Seeds/plants (and other garden supply) costs
data("garden_spending")
# Planting dates and locations
data("garden_planting")
# Tidy Tuesday dog breed data
breed_traits <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_traits.csv')
trait_description <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/trait_description.csv')
breed_rank_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_rank.csv')
# Tidy Tuesday data for challenge problem
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')
Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):
keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).Put your name at the top of the document.
For ALL graphs, you should include appropriate labels.
Feel free to change the default theme, which I currently have set to theme_minimal().
Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!
When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.
These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.
garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.garden_harvest %>%
group_by(vegetable, Day = wday(date, label = TRUE)) %>%
summarise(daily_weight = sum(weight)) %>%
pivot_wider(names_from = Day, values_from = daily_weight)
garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?garden_harvest %>%
group_by(variety) %>%
summarise(tot_harvest = sum(weight)) %>%
left_join(garden_planting)
The problem here is that we don’t know how much of a certain variety of vegetable was harvested in a specific plot; we only know the correlation between plot type and vegetable variety, but have no info on weight. We can fix this by adding a variable in garden_planting that either gives us the portion of a specific variety of vegetable hat was harvested at that plot or the specific weight.
garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.I would first group the garden_harvest data by variety since date no longer matters and summarise the weight for each variety of vegetable. Then I would add the garden_spending data using left_join() so that we get the cost of each variety of vegetable and the weight used in one table. We can now add the selling price of each variety of vegetable per lb using left_join() and then create a column using mutate that multiplies the price per lb by the weight we harvested to get the price we would pay, which we can compare to the price we actually used to buy the vegetables.
garden_harvest %>%
filter(vegetable == "tomatoes") %>%
group_by(variety) %>%
summarise(first_harvest = first(weight)) %>%
ggplot(aes(x = first_harvest, y = fct_rev(fct_reorder(variety, first_harvest))) )+
geom_col() +
ggtitle("Tomato Varieties' First Harvest Weight") +
ylab("Tomato Variety") +
xlab("First Harvest Count")
garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().garden_harvest %>%
mutate(variety_lower = str_to_lower(variety),
variety_length = str_length(variety)) %>%
arrange(vegetable, variety_length) %>%
distinct(variety_lower, .keep_all = TRUE)
garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().garden_harvest %>%
distinct(variety) %>%
filter(str_detect(variety, "er|ar"))
In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.
A typical Capital Bikeshare station. This one is at Florida and California, next to Pleasant Pops.
One of the vans used to redistribute bicycles to different stations.
Two data tables are available:
Trips contains records of individual rentalsStations gives the locations of the bike rental stationsHere is the code to read in the data. We do this a little differently than usual, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.
data_site <-
"https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds"
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")
NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.
It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:
sdate. Use geom_density().Trips %>%
ggplot(aes(x = sdate)) +
geom_density() +
xlab("date") +
ggtitle("Density Plot of Bikes Being Rented Each Day")
This plot shows us how often bikes were rented between Oct and Jan. As we can see, the traffic decreased from Oct to Jan. This is most likely because it started to get cold, and less people were using bikes.
mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(Hour = hour(hms(time)),
Min = minute(hms(time)),
time_float = Hour + Min/60) %>%
ggplot(aes(x = time_float) )+
geom_density() +
xlab("Military Time") +
ggtitle("Density Plot of Bikes Being Rented By Time Of Day")
This plot shows us how often bikes were rented ,on average, each day. AS we can see, there are spikes in activity during 08:00 and 18:00. This is around the time people go to /come back from work, so this makes sense.
Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(weekday = wday(date, label = TRUE)) %>%
ggplot(aes(y = weekday)) +
geom_bar() +
ggtitle("How often Bikes are Rented Each Day of the Week between Oct and Jan")
This bar graph shows us how often bikes were rented each day of the week between Oct and Jan
Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(Hour = hour(hms(time)),
Min = minute(hms(time)),
time_float = Hour + Min/60,
weekday = wday(date, label = TRUE)) %>%
ggplot(aes(x = time_float)) +
geom_density() +
facet_wrap(vars(weekday)) +
ggtitle("Density Plot of Bikes Being Rented By Time Of Day, Seperated by Weekday")
Yes, there is a pattern. for weekdays, there is a higher density for bike usage around 08:00 and 18:00. This makes sense since this is around the time people go to work and leave from work. This is not present for weekends, which also makes sense since most people don’t work on weekends. The pattern present on weekends is that there is rising activity from early morning up until early afternoon around 02:00 , after which the density begins to slowly decline.
The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.
fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(Hour = hour(hms(time)),
Min = minute(hms(time)),
time_float = Hour + Min/60,
weekday = wday(date, label = TRUE)) %>%
ggplot(aes(x = time_float, alpha = .5, fill = client, color = "NA")) +
geom_density() +
facet_wrap(vars(weekday)) +
ggtitle("Density Plot of Bikes Being Rented By Time Of Day, Seperated by Weekday and Client Type")
position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(Hour = hour(hms(time)),
Min = minute(hms(time)),
time_float = Hour + Min/60,
weekday = wday(date, label = TRUE)) %>%
ggplot(aes(x = time_float, alpha = .5, fill = client, color = "NA")) +
geom_density(position = position_stack()) +
facet_wrap(vars(weekday)) +
ggtitle("Density Plot of Bikes Being Rented By Time Of Day, Seperated by Weekday and Client Type")
I think that this is better. The previous graph does not tell us much in terms of casual vs registered. However, in this graph, we can tell that the amount of casual bikers is more than the amount that are registered, on average.
position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(Hour = hour(hms(time)),
Min = minute(hms(time)),
time_float = Hour + Min/60,
Day = wday(date),
weekend = ifelse(Day == 1 | Day == 7, "Weekend", "Weekday")) %>%
ggplot(aes(x = time_float, alpha = .5, fill = client, color = "NA")) +
geom_density() +
facet_wrap(vars(weekend)) +
ggtitle("Density Plot of Bikes Being Rented By Time Of Day, Organized by Weekday/Weekend and Client Type")
client and fill with weekday. What information does this graph tell you that the previous didn’t? Is one graph better than the other?Trips %>%
separate(sdate,
into = c("date", "time"),
sep = "[[:space:]]") %>%
mutate(Hour = hour(hms(time)),
Min = minute(hms(time)),
time_float = Hour + Min/60,
Day = wday(date, label = TRUE),
weekend = ifelse(Day == 1 | Day == 7, "Weekend", "Weekday")) %>%
ggplot(aes(x = time_float, alpha = .5, fill = Day, color = "NA")) +
geom_density() +
facet_wrap(vars(client)) +
ggtitle("Density Plot of Bikes Being Rented By Time Of Day, Organized by Client Type and Weekday")
This graph has a lot of overlapping values since a lot of the days have similar dat (like weekdays and weekends). However, this data is useful in that it distinguishesthe data by caual and registered users. This data is not better than the previous data, since it is hard to distinguish each individual date, and it is better to group the days into 2 catagories: weekend and weekday.
Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!Trips %>%
left_join(Stations, by = c("sstation" = "name")) %>%
select(sstation,lat,long) %>%
group_by(lat,long,sstation) %>%
summarise(num_stations = n()) %>%
ggplot(aes(x=lat, y=long, size = num_stations)) +
geom_point() +
ggtitle("Visualization of Number of Departures from Different Stations") +
xlab("Latitude") +
ylab("Longitude")
Trips %>%
left_join(Stations, by = c("sstation" = "name")) %>%
filter (client == "Casual") %>%
group_by(lat,long,sstation) %>%
summarise(num_stations = n())%>%
ggplot(aes(x=lat, y=long, size = num_stations)) +
geom_point()+
ggtitle("Visualization of Number of Departures made by Casual users from Different Stations") +
xlab("Latitude") +
ylab("Longitude")
DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.
In this section, we’ll use the data from 2022-02-01 Tidy Tuesday. If you didn’t use that data or need a little refresher on it, see the website.
breed_traits dataset on the x-axis, with a dot for each rating. First, create a new dataset called breed_traits_total that has two variables – Breed and total_rating. The total_rating variable is the sum of the numeric ratings in the breed_traits dataset (we’ll use this dataset again in the next problem). Then, create the graph just described. Omit Breeds with a total_rating of 0 and order the Breeds from highest to lowest ranked. You may want to adjust the fig.height and fig.width arguments inside the code chunk options (eg. {r, fig.height=8, fig.width=4}) so you can see things more clearly - check this after you knit the file to assure it looks like what you expected.breed_traits_total <- breed_traits %>%
rowwise() %>%
summarise(total_rating = sum(c(`Affectionate With Family`,
`Good With Young Children`,
`Good With Other Dogs`,
`Shedding Level`,
`Coat Grooming Frequency`,
`Drooling Level`,
`Openness To Strangers`,
`Playfulness Level`,
`Watchdog/Protective Nature`,
`Adaptability Level`,
`Trainability Level`,
`Energy Level`,
`Barking Level`,
`Mental Stimulation Needs`)),
Breed = Breed)
breed_traits_total %>%
filter (total_rating != 0) %>%
ggplot(aes(y= fct_reorder(Breed,total_rating), x= total_rating)) +
geom_point() +
ggtitle("Dog Breeds From Highest Total Rating to Lowest")
breed_rank_all dataset). The points within each breed will be connected by a line, and the breeds should be arranged from the highest median rank to lowest median rank (“highest” is actually the smallest numer, eg. 1 = best). After you’re finished, think of AT LEAST one thing you could you do to make this graph better. HINTS: 1. Start with the breed_rank_all dataset and pivot it so year is a variable. 2. Use the separate() function to get year alone, and there’s an extra argument in that function that can make it numeric. 3. For both datasets used, you’ll need to str_squish() Breed before joining.breed_rank_all_squished <- breed_rank_all %>%
mutate(squished_Breed = str_squish(Breed)) #remove white space from the second dataset
breed_traits_total %>%
top_n(20) %>%
mutate(squished_Breed = str_squish(Breed)) %>% #remove white space from first dataset
left_join(breed_rank_all_squished, by = "squished_Breed") %>%
pivot_longer(cols = starts_with("20"),
names_to = "Year",
values_to = "Rank") %>%
select(squished_Breed, total_rating, Year, Rank) %>%
separate("Year",
into = c("year"),
convert = TRUE) %>%
ggplot(aes(y = squished_Breed, x = year, color = Rank)) +
geom_point() +
ggtitle("Top 20 Dogs by total ratings and their Ranks from 2013 to 2020") +
ylab("breed")
join or pivot function (or both, if you’d like), a str_XXX() function, and a fct_XXX() function to create a graph using any of the dog datasets. One suggestion is to try to improve the graph you created for the Tidy Tuesday assignment. If you want an extra challenge, find a way to use the dog images in the breed_rank_all file - check out the ggimage library and this resource for putting images as labels.breed_traits %>%
mutate(Coat_length = str_sub(`Coat Length`, 1, 1)) %>% # abbreviate coat length
pivot_longer(cols = -c(Breed, `Coat Length`, `Coat Type`, Coat_length),
names_to = "Trait",
values_to = "Rating") %>%
select(Coat_length, `Coat Type`,Trait, Rating) %>%
filter(Coat_length %in% c("S", "M","L"), #exclude plott hounds
Trait %in% c("Shedding Level")) %>%
group_by(Coat_length, `Coat Type`) %>%
summarise(avg=mean(Rating)) %>%
ggplot(aes(x = avg , y = fct_reorder(`Coat Type`,avg), fill = Coat_length)) +
geom_col() +
ggtitle("How does Coat Type Affect Shedding and Grooming, Organized by Length") +
xlab("Avg Shedding Lvl ") +
ylab("Coat Type") +
facet_wrap(vars(Coat_length))
here.
This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.
facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?